Rain sensor for detecting rain or other material on window of a vehicle or on other surface

ABSTRACT

A system and/or method for sensing the presence of moisture (e.g., rain) and/or other material(s) on a window such as a vehicle window (e.g., vehicle windshield or backlite). In certain example embodiments, a capacitor-based system and/or method is provided for auto-correlating sensor data to determine the existence of a material on the window, and then cross-correlating sensor data to identify the type and/or amount of that material (e.g., rain). This data may be used to actuate and/or deactivate a vehicle&#39;s wiper(s), and/or adjust wiper speed. In certain example embodiments, the sensor may include an array of at least two capacitors. In certain example embodiments, the system and/or method may perform check(s) to enhance the accuracy of the detection by comparing, for example, the sign of autocorrelation values, maximum autocorrelation values, and/or gradients of autocorrelation values

This application claims priority on U.S. Provisional Patent Application No. 60/757,479, filed Jan. 10, 2006.

FIELD OF THE INVENTION

This invention relates to a system and/or method for sensing the presence of rain and/or the disturbances or presence of other materials on a sheet(s) of glass such as a vehicle windshield. In certain example non-limiting embodiments, a system and/or method is provided for auto-correlating sensor data (e.g., data from one or more capacitors) to determine whether it is likely that a material such as moisture (e.g., water) is present on a sheet of glass. Optionally, in certain example embodiments, cross-correlation of sensor data may be performed to identify the type and/or amount of material present on the glass. In certain example embodiments, cross-correlating may be used without auto-correlating. In certain example embodiments, results of the correlation(s) may be used to adjust a vehicle's wiper speed, and/or a wiper's actuation and/or stoppage.

BACKGROUND AND SUMMARY OF EXAMPLE EMBODIMENTS OF THE INVENTION

The presence of moisture (e.g., rain or condensation) and/or other material or debris on vehicle windshields and/or backlites may create hazardous driving conditions for drivers, passengers, and pedestrians if not promptly removed. Wiper blades are a well-known, common way to remove such materials and reduce the hazards of driving during dangerous conditions. Rain sensors have been developed to detect the presence of moisture (e.g., rain or other condensation) on a vehicle windshield, and to turn on and off wipers, as necessary, when such moisture is detected. Automatically detecting rain, sleet, fog, and the like, and taking appropriate action—for example, turning on/off wiper blades at a proper speed—potentially reduces distractions to the driver, allowing the driver to better concentrate on the road ahead. However, inappropriately turning on/off wipers or failing to actuate wipers when moisture is present may also create hazardous conditions. Moreover, such systems are also susceptible to “dirt” distractions which may cause false reads/wipes when dirt is on the windshield.

Certain conventional rain sensors are based on an electro-optical concept. According to certain such techniques, rain droplets are sensed solely by measuring the change in the total internal reflection of a light beam off the glass-air interface. Other electro-optical techniques have attempted to analyze the brightness of a section of a window “image” to detect rain droplets or fog on a window. However, these optical techniques have limited sensing areas, are fairly expensive, and may result in erroneous detection indications due to the use of optical imaging as the sole detection method.

U.S. Pat. No. 6,144,022 to Tenenbaum et al. discloses an optical technique for sensing rain on a vehicle windshield. This optical system divides a windshield into discrete rows and columns of pixels and then optically develops an “image” of the windshield. It creates a reference image of the windshield against which it compares future optical images. Unfortunately, the system of Tenenbaum suffers from certain disadvantages. The Tenenbaum optical system is susceptible to erroneous detections due to its reliance solely on optical imaging, and has a limited sensing area. The resolution of the optical image, and thus the overall accuracy of the system, is dependent on the imaging optics. This necessitates expensive optical components while requiring computationally intense data analysis, while the system is still subject to the above disadvantages. Furthermore, Tenenbaum depends on the existence of light to illuminate the water droplets through ambient means. Without naturally occurring ambient light (e.g., at night), the system will not properly function. LEDs may be used, but this tends to make the system more complex and/or expensive, with additional potential points of failure. Moreover, when using LEDs in the manner disclosed by Tenenbaum, the system can be confused by sudden changes in ambient light. For example, sudden changes in ambient light may occur when going through a tunnel, coming around a corner and suddenly facing the sun, driving through a city with skyscrapers that block the sun, etc., thereby leading to a potential for false readings/detections and false wiper actuations.

U.S. Pat. No. 6,373,263 to Netzer teaches using capacitive rain sensors and reading the differential current between two capacitors on the windshield. Unfortunately, Netzer's system also has significant disadvantages. For example, Netzer's system is sensitive only to changes. Thus, for example, if there is already moisture (e.g., rain or condensation) on a windshield because a vehicle was parked outside during a rain shower or fog, Netzer's system may not detect the same when the vehicle is started. Moreover, Netzer's system may be subject to certain detrimental effects of electromagnetic interference (EMI), temperature changes, as well as interference from other sources. For instance, as external bodies (e.g., human hand, radio waves, etc.) interfere with the function of the capacitors, the charges of the excitation and receiver electrodes may uncontrollably vary in Netzer, thereby leading to false alarms or detections. Thus, for example and without limitation, with Netzer's system, CB radios, microwaves, handheld devices, human contact with the windshield, groundable objects, and/or the like may undesirably interfere with the system, and thus possibly produce false wipes and/or detections. Netzer's system is also subject to possible false reads caused by drastic temperature changes in view of the reference capacitor system utilized by Netzer, where Netzer's reference capacitor has a different geometry/shape/size than the sensing capacitor.

Thus, it will be appreciated that there exists a need in the art for a moisture (e.g., rain) sensor that is efficient in operation and/or detection. For example and without limitation, it may be desirable to provide a rain sensor that overcomes one or more of the above-discussed disadvantages. It is noted that all of the above-discussed disadvantages need not be overcome in certain example embodiments of this invention.

In certain example embodiments of this invention, there is provided a method of sensing the presence of moisture (e.g., rain, dew, fog, or the like) on a vehicle window, the method comprising: receiving data relating to at least two capacitors supported by the vehicle window; autocorrelating the data relating to each capacitor to obtain autocorrelated data; and determining, based at least on said autocorrelated data, whether moisture is present on an exterior surface of the vehicle window. In certain example embodiments, the data relating to the at least two capacitors is received from circuitry that receives and/or reads capacitance data from the at least two capacitors. In certain example embodiments, the data relating to the at least two capacitors is output from circuitry that: (a) receives and/or reads data and/or signals from the at least two capacitors, and/or (b) includes a capacitor(s) or other circuit element(s) that mimics or substantially mimics charging and/or discharging of the at least two capacitors. In certain example embodiments, the autocorrelation may be used as an initial step to determine whether water may be present on the window. However, it is possible that the autocorrelation may also detect the presence of other materials (e.g., dust or dirt) on the window because the correlation signatures of these materials can be different.

In certain example embodiments of this invention, there is provided a moisture sensor (e.g., rain sensor) for sensing the presence of moisture on a vehicle window, the moisture sensor comprising: one, two or more capacitors; means for autocorrelating data from one, two, three, more, or all of the capacitors to obtain autocorrelated data; and means for determining, based at least on said autocorrelated data, whether moisture is present on the vehicle window.

In certain example embodiments of this invention, cross-correlating data from the at least two capacitors may be performed so as to correlate data from different capacitors to obtain cross-correlated data. Then, based at least on the cross-correlated data, a type and/or amount of moisture may be determined. The cross-correlated data may also or instead be used to determine if the material detected via the autocorrelation is a material other than moisture such as dust or dirt, and if so then not actuating the wipers. In certain example embodiments, the cross-correlating may be performed after the autocorrelating when certain conditions are met. As an example, the cross-correlation may be performed so as to determine whether the moisture on the window is light rain, heavy rain, fog, sleet, snow, or ice (a type of moisture).

In certain example embodiments of this invention, the autocorrelated data from the capacitor(s) may be checked for negative values. When the autocorrelated data has negative value(s), then the system or method may indicate that it is not raining and/or may not actuate windshield wipers.

Moreover, in certain example embodiments, the system or method may calculate whether a gradient of an autocorrelation curve associated with the autocorrelated data is greater than one or some other predetermined value; and if not then the system or method may indicate that it is not raining, park the wipers if they were moving, and/or not actuate wipers of the vehicle.

In certain example embodiments of this invention, the system or method may determine whether the shape of the autocorrelation curve associated with the autocorrelated data is different than a predetermined autocorrelation curve associated with normalized non-disturbed autocorrelation data. When it is not different or substantially different, then it may be indicated that it is not raining, wipers may be parked if they had been moving, and/or wipers may be not actuated.

In certain example embodiments of this invention, conditions checked for in the autocorrelation function include (i) the gradient of the normalized autocorrelation function (e.g., when there is no disturbance the absolute value of the gradient is unity and changes with disturbance), (ii) the sign of the autocorrelation function (e.g., with a CB radio turned on or with a human hand on the windshield the values are oscillatory with positive and negative parts), and (iii) the shape of the autocorrelation function as a function of time lag may also be used as a signature or footprint to distinguish rain from other disturbances, and this shape may also be used to distinguish between different nuances of rain or water content. Thus, in certain example instances, cross-correlating of data from at least two capacitors is only performed when one, two or all of the following conditions are met: (a) the autocorrelated data has no negative values; (b) a gradient of an autocorrelation curve associated with said autocorrelated data is greater than one; and (c) the shape of the autocorrelation curve associated with the autocorrelated data is different than a predetermined autocorrelation curve associated with normalized non-disturbed autocorrelation data. Alternatively, (c) may be replaced with (c′) the shape of the autocorrelation curve associated with the autocorrelated data matches or substantially matches a predetermined autocorrelation curve associated with a known moisture pattern. In certain example embodiments of this invention, a symmetry level of a cross-correlation curve associated with the cross-correlated data can be determined.

In certain example embodiments of this invention, it is possible to compare the autocorrelation between various capacitors. In certain example embodiments of this invention, such a comparison may be used to tell the system whether to initiate a wipe if water is present on the window when the sensor system is turned on.

In certain example embodiments, a sensing capacitor array may include at least n capacitors, where n may be two, four, ten or any other suitable number. The array may be any type of array such as a linear array, any of the arrays shown in the figures, or any other type of array. Autocorrelating of data from each of, or less than all of, the capacitors may be performed to obtain the autocorrelated data.

In certain example embodiments of this invention, capacitors are formed based on a fractal pattern. For example and without limitation, one or more of the capacitors may be formed based on a fractal pattern, such as a Hilbert fractal pattern. Other capacitive fractal patterns may also be used, including but not limited to a Cantor set. These fractal structures maximize or enlarge the periphery and thus result in a large capacitance for a given area. The use of two dimensional fractal designs also allows the sensor to occupy a small amount of physical space on the window while at the same time being electrically larger than its physical size. The concentration of lateral flux in a fractal geometry may also allow the sensor to detect rain/water not necessarily spread over the actual physical area of the sensor in certain example embodiments of this invention. Furthermore, in its higher iteration(s) a fractal capacitor(s) has an attribute of being its own Faraday shield or quasi-Faraday shield. Also, in certain example embodiments, the rain sensor may be electrically connected to a Local Interconnect Bus of the vehicle.

In certain example embodiments of this invention, there is provided a method of sensing the presence of moisture on a vehicle window such as a windshield, backlite or sunroof, the method comprising: receiving data from at least two capacitors supported by the vehicle window; correlating data from one or more of the capacitors to obtain correlated data; determining, based at least on said correlated data, (a) whether moisture is present on an exterior surface of the vehicle window, and/or (b) a type and/or amount of material present on an exterior surface of the vehicle window. For example and without limitation, the correlation may be autocorrelation and/or cross-correlation.

In certain example embodiments of this invention, there is provided a method of engaging vehicle windshield wiper(s) in response to detected rain, the method comprising reading data from a capacitive array having at least two capacitors; autocorrelating data from each capacitor individually; determining from the autocorrelation data whether it is raining; cross-correlating data from the capacitors; determining from the cross-correlated data a type and/or an amount of rain; engaging the wipers if rain is detected; and, stopping or not actuating the wipers if one or both of the determining steps determines that it is not raining. In certain example embodiments, a symmetry level of the cross-correlation curve may be determined, and a wiper speed related to the symmetry level may be selected. A wiper speed may be selected from a plurality of predetermined wiper speeds in certain example instances. In some example embodiments, only a single wipe is initiated for boundary conditions detected in one or both of the determining steps.

In certain example embodiments of this invention, there is provided a method of engaging windshield wipers of a vehicle in response to detected rain, the method comprising reading data from a capacitive array having at least two capacitors; mathematically comparing data from each capacitor individually (e.g., autocorrelating); determining from the mathematically compared individual capacitor data whether it is raining; mathematically comparing data from different capacitors (e.g., cross-correlating); determining from the mathematically compared different capacitor data a type and/or an amount of rain; engaging the wipers if rain is detected; and, stopping or not actuating the wipers if one or both of the determining steps determines that it is not raining.

In certain example embodiments, a sigma-delta modulator or other suitable circuit or software may be used to perform an analog-to-digital (A/D) conversion of data from the capacitive array. Additionally, in certain example embodiments, a software or other type of comparator may perform at least one of checking autocorrelation data for negative values, calculating whether a gradient of autocorrelation data is greater than one, and/or attempting to match or substantially match a shape of autocorrelation data with autocorrelation data stored in a database. In certain instances, the correlating engine computes cross-correlations when all conditions tested for by the comparator are met.

In certain example embodiments, a rain sensor comprises at least two sensing devices (e.g., sensing capacitors or the like) that are affected by rain on a surface of a window; circuitry that provides an output related to the sensing devices; and at least one correlating engine that (a) autocorrelates information from said circuitry to determine whether rain is present, and/or (b) cross-correlates information from said circuitry to determine how fast to operate at least one wiper of a vehicle and/or an amount of rain.

In certain example embodiments of this invention, there is provided a system or method for engaging windshield wipers in response to detected rain, the system (or method) comprising a capacitive array having at least two capacitors; circuitry that reads capacitance data from the capacitive array; a correlating engine or correlator that autocorrelates data from the circuitry to determine the existence of rain, and cross-correlates data from the circuitry to determine a type and/or an amount of rain if it is determined that rain exists; and, a wiper motor that is capable of receiving a signal for directing whether the wipers should move or stop. In certain example embodiments, a symmetry level of a cross-correlation curve is computed, and the wiper motor may select a wiper speed related to the symmetry level.

In certain example embodiments, a method or system for engaging window wiper(s) in response to detected rain is provided and comprises a capacitive array having at least two capacitors; circuitry that reads capacitance data from the capacitive array; an algorithm that mathematically determines existence of rain on the window based on data from the circuitry, and mathematically quantifies a type and/or amount of rain if it is determined that rain exists; and, a wiper motor capable of receiving a signal(s) directing whether the wiper(s) should move or stop.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages will be better and more completely understood by reference to the following detailed description of exemplary illustrative embodiments in conjunction with the drawings, of which:

FIG. 1(a) is a block diagram of components of an exemplary rain sensor according to an example embodiment of this invention.

FIG. 1(b) is a cross sectional view of a rain sensor according to an example embodiment of this invention, that may use the features of FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 1(c) is a cross sectional view of a rain sensor according to another example embodiment of this invention, that may use the features of FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 1(d) is a cross sectional view of a rain sensor according to another example embodiment of this invention, that may use the features of FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 1(e) is a cross sectional view of a rain sensor according to another example embodiment of this invention, that may use the features of FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 1(f) is a cross sectional view of a rain sensor according to another example embodiment of this invention, that may use the features of FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 2A is an exemplary optimized pattern for a quadrant capacitive array based on Hilbert fractals, where such capacitors may be provided on the window as a sensor array in the embodiments of one or more of FIGS. 1(a)-1(f) and 4-12 for example.

FIG. 2B is another exemplary optimized pattern for a quadrant capacitive array, where such capacitors may be provided on the window as a sensor array in the embodiments of one or more of FIGS. 1(a)-1(f) and 4-12 for example.

FIG. 3 is an enlarged picture of yet another exemplary quadrant capacitive array, where such capacitors may be provided on the window as a sensor array in the embodiments of one or more of FIGS. 1(a)-1(f) and 4-12 for example.

FIG. 4 is an example circuit diagram including exemplary circuitry used for a write clock pulse in readout electronics, for use in one or more of the embodiments of FIGS. 1(a)-1(f) and 5-12 for example.

FIG. 5 is an example circuit diagram including exemplary circuitry used for an erase clock pulse in readout electronics, for use in one or more of the embodiments of FIGS. 1(a)-1(f), 4 and 6-12 for example.

FIG. 6 is an exemplary timing diagram derived from readout circuitry of FIGS. 4-5.

FIG. 7 is an exemplary flowchart or state diagram showing how autocorrelation and cross-correlation data may be used to control wipers according to an example embodiment of this invention, which may be used in conjunction with one of more of FIGS. 1-6 and 8-12.

FIG. 8 is an exemplary flowchart showing how autocorrelation and cross-correlation data can be used to control wipers according to an example embodiment of this invention, which may be used in conjunction with one of more of FIGS. 1-7 and 9-12.

FIG. 9 is an exemplary stylized view of how a rain droplet might travel across a windshield.

FIG. 10 is an graph plotting example experimentally-obtained maximum values of non-normalized autocorrelations for different disturbances.

FIG. 11A is an example experimentally-obtained autocorrelation snapshot indicative of heavy rain.

FIG. 11B is an example experimentally-obtained autocorrelation snapshot indicative of a light mist.

FIG. 11C is an example experimentally-obtained autocorrelation snapshot indicative of CB radio interference.

FIG. 11D is an example experimentally-obtained autocorrelation snapshot indicative of a grounded body with a voltage.

FIG. 12A is an exemplary correlation matrix indicative of light rain.

FIG. 12B is an exemplary correlation matrix indicative of heavy rain.

FIG. 13 is an example of autocorrelation according to an example embodiment of this invention.

FIG. 14 is a chart setting forth example cross-correlation data from capacitors C1, C2 according to examples of certain embodiments of this invention.

FIG. 15 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 16 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 17 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 18 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 19 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 20 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 21 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 22 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 23 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 24 is a crosscorrelation graph, plotting crosscorrelation values versus time lags (the time lags are in terms of microseconds in the time domain) according to an example of this invention, using certain signals from FIG. 14.

FIG. 25 is a block diagram illustrating circuitry and/or processing of signals according to an example embodiment of this invention where a sensing capacitor (e.g., C1) is present, including sigma-delta modulation.

FIG. 26 is a block diagram illustrating circuitry and/or processing of signals according to an example embodiment of this invention where a plurality of capacitors (e.g., C1-C4) are present, including sigma-delta modulation.

FIG. 27 is a block diagram illustrating sigma-delta modulation according to an example embodiment of this invention; this processing being performed in circuitry, firmware and/or software.

FIGS. 28(a) AND 28(b) are schematic diagrams illustrating advantages of using floating electrodes for sensing capacitors (e.g., C I—C4) according to certain example embodiments of this invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

Referring now more particularly to the accompanying drawings in which like reference numerals indicate like parts throughout the several views.

In certain example embodiments of this invention, a moisture (e.g., rain) sensor system and/or method is provided and includes capacitance-based detection which translates a physical input signal (e.g., the presence of a drop of water on a windshield, or the like) into a digital electrical voltage signal which is received and interpreted by a software program(s) or circuit(s) that decides whether windshield wipers should be activated, and, if so, optionally their proper speed. Thus, capacitive coupling is used to detect water and/or other material in the exterior surface of a window such as a vehicle windshield, sunroof, and/or backlite. It will be appreciated that computational methods may be performed by hardware or a combination of hardware and software in different example embodiments of this invention. In certain example embodiments of this invention, no reference capacitance or capacitor is needed (i.e., no compensation capacitor is needed).

Certain example embodiments of this invention take advantage of a permittivity equation, which gives a physical quantity that describes how an electric field affects and is affected by a medium. An example basic permittivity equation is: D=ε ₀ E+P, where D is electrical flux, ε₀ is the dielectric constant of a vacuum, E is an electrical field (e.g., the voltage setup between plates or electrodes divided by distance, or V/m), and P is polarization. Polarization P can be further described mathematically as: P=ε _(r)ε₀ E, where ε_(r) is relative permittivity (e.g., the dielectric constant of water, ice, dirt or anything else that could be on an exterior surface of a window such as a windshield). In general, a high value of ε_(i) will correspond to high polarizability. The permittivity of glass is approximately 8, and the permittivity of water is approximately 85. By substitution and factorization, then, the permittivity equation can be rewritten as: D=ε ₀(ε_(r)+1)E. In this form, it will be appreciated that D is the response to excitation E.

Capacitance C is given by C=Q/V, where Q is the charge and V is the potential, in volts. Additionally, C=Φ/V, where Φ is the electric flux associated with charge Q. By Gauss' Law: Φ = o∫_(S)E ⋅ 𝕕A, where dA is the area of a differential square on the closed surface S. By substitution, then, it becomes clear that capacitance is related to potential difference: C=∫DdA/V.

These equations form the basis of an example technique for measuring the interaction of water on glass by using a sensor with a capacitive array to probe above the window (e.g., glass). In particular, data from a sensor including at least one, or two or more, capacitor(s) (e.g., C1, C2, C3, etc.) may be used to detect whether moisture (e.g., rain, or the like) is present on an exterior surface of a window such as a vehicle windshield or backlite. The above equations illustrate that the presence of water on the surface of a window can affect the capacitance of an appropriately positioned sensing capacitor.

FIG. 1(a) is a block diagram of example components of a moisture (e.g., rain) sensor according to an example embodiment of this invention. Power supply 10 is connected to readout electronics 12 which may include one or more of hardware, firmware, and/or software. As will be described in greater detail below, the sensor includes one or more capacitors so as to make up a capacitive sensor 5 in certain example embodiments. While different types of capacitors may be used, capacitors each having a pair of approximately coplanar electrodes arranged in a fractal pattern may be used in the sensor in certain example embodiments of this invention. In certain example embodiments, a fractal pattern may be divided into a capacitive array. Data from and/or related to the capacitors of the capacitive sensor 5 is received and read by readout electronics 12 which may be made up of one or more of hardware, firmware and/or software. Readout electronics 12 pick up electrical noise and convert the same to digital signal(s). This digital signal(s) is passed to computing module 14 (which may be made up of one or more of hardware, firmware and/or software) which determines what action the wipers should take. For example, the wipers might initiate a single wipe, low-speed wipes, high-speed wipes, etc., based on the data analyzed from and/or related to the capacitive sensor. The wipers also may be caused to turn off, slow/increase the speed at which they are wiping, etc., based on the data analyzed from and/or related to the capacitive sensor. Wiper control system motor 16 receives instructions from computing module 14 and directs wipers 18 to take the appropriate action.

In certain example embodiments, the capacitive sensor 5 interfaces with a Local Interconnect Bus (LIN bus) of a vehicle. A LIN bus (not shown) typically is a serial bus to which slave devices in an automobile are connected. A LIN bus typically executes a handshake(s) with slave devices to ensure that they are, for example, connected and functional. Additionally, a LIN bus may provide other information to slave devices, such as, for example, the current time.

In certain example embodiments of this invention, the capacitive sensor 5 includes a plurality of capacitors in the form of any suitable array.

FIG. 1(b) is a cross-sectional view of a vehicle window including a moisture sensor according to an example embodiment of this invention. A windshield of the vehicle includes inner glass substrate 1 and outer glass substrate 2 that are laminated together via a polymer-inclusive interlayer 3 of a material such as polyvinyl butyral (PVB) or the like. An optional low-e (low emissivity) coating 4 may be provided on the inner surface of the exterior glass substrate 2 (or even on the surface of substrate 1) in certain example embodiments of this invention. A low-E coating 4 typically includes at least one thin IR reflecting layer of a material such as silver, gold or the like sandwiched between at least first and second dielectric layers of material such as silicon nitride, tin oxide, zinc oxide, or the like. Example low-E coatings 4, for purposes of example and without limitation, are described in U.S. Pat. Nos. 6,686,050, 6,723,211, 6,782,718, 6,749,941, 6,730,352, 6,802,943, 4,782,216, 3,682,528, and 6,936,347, the disclosures of which are hereby incorporated herein by reference.

FIG. 1(b) illustrates an example capacitor of the capacitive sensor. While the capacitive sensor of FIG. 1(a) typically includes a plurality of capacitors in an array, only one capacitor of the sensor is shown in FIG. 1(b) for purposes of simplicity. The other capacitors are similar in cross section to the one shown in FIG. 1(b) in certain example embodiments of this invention. The example capacitor (C1, C2, C3 or C4) of the capacitive sensor shown in FIG. 1(b) includes a pair of spaced apart coplanar or substantially coplanar capacitor electrodes 7 and 8. The electrodes 7 and 8 are of a conductive material that may be printed or otherwise formed on the window. For example, the capacitor electrodes 7 and 8 of the sensing capacitor may be made of or include silver, ITO (indium tin oxide), or other suitable conductive material. In certain example embodiments, the capacitor shown in FIG. 1(b) is affected by a rain droplet on the exterior surface of the window because electric field Es of the capacitor extends to or beyond the exterior surface of the window as shown in FIG. 1(b) and thus can interact with the rain droplet or other material on the window's exterior surface. Signals received from and/or related to the capacitors and analysis thereof is described herein.

In the FIG. 1(b) embodiment, an opaque insulating layer (e.g., black frit or enamel, or the like) 9 is provided on the window over the electrodes 7 and 8 in order to shield the electrodes 7, 8 from the view of a passenger(s) sitting inside the vehicle. Thus, it will be appreciated that the opaque layer 9 is only provided on a small portion of the window, including in the area where the capacitive array of the rain sensor's array of capacitors is located. In certain example instances, the rain sensor's capacitive array and thus the opaque layer 9 may be located on a vehicle windshield in an area proximate the rear-view mirror mounting bracket. In certain example embodiments, the opaque layer 9 (e.g., black frit or enamel) may contact the fractal pattern of the capacitor electrodes 7, 8 directly because the layer 9 is not conductive. However, even if a black frit layer 9 were conductive (which is possible), its dielectric constant is close to that of water so that it will not adversely interfere with the capturing of data from and/or related to the capacitors C1-C4 and associated analysis.

FIG. 2A is a top or plan view illustrating an example capacitive sensor array including four capacitors C1, C2, C3 and C4. Each of these capacitors C1, C2, C3 and C4 includes first and second spaced apart coplanar capacitor electrodes 7 and 8 as shown in FIG. 1(b) (or any of FIGS. 1(c)-1(f)). The capacitor electrodes 7 and 8 of each capacitor C1-C4 may be made of conductive silver frit or the like as shown in FIG. 2A. Moreover, in certain example embodiments, there may be a gap 22 of from about 0.2 to 1.5 mm, more preferably from about 0.3 to 1.0 mm (e.g., 0.6 mm), between the coplanar capacitor electrodes 7 and 8 of a capacitor (C1, C2, C3 and/or C4) as shown in FIG. 2A. In the FIG. 2A embodiment, the capacitors C1-C4 are covered with an insulating black frit layer 9 which is the same as the opaque layer 9 discussed above with respect to FIG. 1(b). In FIG. 2A, a contact pad array is provided in the center of the sensor array, and includes four contact pads electrically connected to the respective electrodes 7 of the capacitors C1-C4, and four contact pads electrically connected to the respective electrodes 8 of the capacitors C1-C4. An example contact pad is referred to by reference numeral 28 in FIG. 2A. The four white colored contact pads 28 in FIG. 2A are electrically connected to the respective capacitor electrodes 7 of capacitors C1-C4, whereas the dark grey colored contact pads 28 in FIG. 2A are electrically connected to the respective capacitor electrodes 8 of the capacitors C1-C4. All of sensing capacitors C1-C4 are sensitive to moisture on the external surface of the window.

In the FIG. 2A embodiment, each of the capacitors C1-C4 of the capacitive sensor is formed using fractal geometry. In particular, each of the coplanar electrodes 7 and 8 of each capacitor C1-C4 is formed with a fractal geometry. Fractal design patterns allow, for example, a high capacitance to be realized in a small area, and are therefore desirable over other geometries in certain example rain sensor applications. Fractal geometry may be grouped into (a) random fractals, which may be called chaotic or Brownian fractals and include a random noise component, and (b) deterministic or exact fractals. In deterministic fractal geometry, a self-similar structure results from the repetition of a design or motif (or “generator”) (i.e., self-similarity and structure at all scales). In deterministic or exact self-similarity, fractal capacitors may be constructed through recursive or iterative means. In other words, fractals are often composed of many copies of themselves at different scales.

In the FIG. 2A embodiment, it can be seen that the coplanar electrodes 7 and 8 of each capacitor (where the electrodes 7 and 8 are shown but not labeled in FIG. 2A due to the dark color of the frit 9, but are spaced apart by gaps 22) have fractal geometries and are arranged substantially parallel to each other throughout the meandering length of each capacitor. In other words, each electrode 7, 8 of a given capacitor (e.g., C1, C2, C3 or C4) has a meandering shape in the fractal geometry, but stays substantially parallel to the other electrode (the other of 7, 8) of the capacitor throughout the meandering length of the capacitor. The overall length of each capacitor (e.g., C1), along the meandering length of the fractal, is from about 25 to 200 mm in certain example embodiments of this invention, more preferably from about 30 to 90 mm, with an example being about 50 mm.

The fractal pattern of FIG. 2A is a Hilbert fractal pattern. The electrodes 7, 8 of the capacitors C1-C4 in the FIG. 2A embodiment form a Hilbert fractal pattern, for purposes of example only and without limitation. In particular, the capacitors shown in FIG. 2A are shaped in a third-order Hilbert fractal manner. Hilbert fractals are continuous space-filling fractals, with fractal dimensions of two. This means that higher-order fractals will become more square-like. A Hilbert fractal can be formed by using the following L-system: Hilbert {   Angle 90   Axiom X   X = −YF+XFX+FY−   Y = +XF−YFY−FX+ } where “Angle 90” sets the following rotations to 90 degrees, X and Y are defined functions, “F” means “draw forward”, “+” means “turn counterclockwise”, and “−” means “turn clockwise”. While Hilbert fractal geometries may be used in forming the capacitors C1-C4 in certain example embodiments of this invention, this invention is not so limited, and other types of fractals may also be used to form the capacitor shapes. For example, the capacitor electrodes 7, 8 of capacitors C1-C4 may be formed using any of the fractal designs disclosed in any of U.S. Pat. Nos. 6,552,690, 6,104,349, 6,140,975, 6,127,977, 6,084,285, 6,975,277, the disclosures of which are hereby incorporated herein by reference. In certain example embodiments of this invention, as shown in FIGS. 2A, 2B and 3, all sensing capacitors of the sensing array may be identical or substantially identical in shape.

In preferred embodiments, each of the capacitors C1-C4 in the sensor array may be electrically floating (this may be called a virtual ground in certain example instances) so as to not have a fixed common ground such as a fixed zero volts, and/or spatially separated or the like which may be useful with respect to the correlation functions. Additionally, the lack of a common ground means that the capacitive array will not be subject to adverse effects from interference such as, for example, EMI interference thereby reducing the potential for false wipes, false detections, and the like.

The fractal design for capacitors C I—C4 may be used in any of the embodiments of FIGS. 1(a)-1(f).

FIG. 1(c) is a cross sectional view of another example embodiment of this invention, which may use the system of FIG. 1(a) and one or more of the embodiments of FIGS. 2-12. In the FIG. 1(c) embodiment, the vehicle window (e.g., backlite) is made up of only one glass sheet 10, and the electrodes 7, 8 of the capacitor are provided on, directly or indirectly, the interior major surface of the glass sheet 10. The capacitor (e.g., C1) shown in FIG. 1(c) is designed such that it is affected by a rain droplet (or other material) on the exterior surface of the window because the electric field Es of the capacitor extends to or beyond the exterior surface of the window as shown in FIG. 1(c) and thus can interact with the rain droplet or other material on the window's exterior surface. Each of the capacitors C1-C4 is formed in a similar manner. It is noted that the use of the word “on” herein covers both directly on and indirectly on, and is not limited to physical contact or touching unless expressly stated. An opaque layer 9, similar to that shown in the FIG. 1(b) embodiment, may also be provide in the FIG. 1(c) embodiment if desired.

FIG. 1(d) is a cross sectional view of another example embodiment of this invention, which may use the system of FIG. 1(a) and one or more of the embodiments of FIGS. 2-12. In the FIG. 1(d) embodiment, the vehicle window (e.g., laminated windshield) includes glass sheets 1 and 2 laminated together via polymer based interlayer 3, and optionally includes a low-E coating 4 on either substrate 1 or substrate 2. The FIG. 1(d) embodiment differs from the FIG. 1(b) embodiment in that the electrodes 7, 8 of the capacitor are provided on the major surface of glass substrate 1 that is furthest from the vehicle interior. The capacitor electrodes 7, 8 may contact the polymer interlayer 3 in this embodiment, in certain example instances. The capacitor (e.g., C1, C2, C3 or C4) shown in FIG. 1(d) is designed such that it is affected by a rain droplet (or other material) on the exterior surface of the window because the electric field Es of the capacitor extends to or beyond the exterior surface of the window as shown in FIG. 1(d) and thus can interact with the rain droplet or other material on the window's exterior surface. Each of the capacitors C1-C4 of the sensor array is formed in a manner similar to that shown for the capacitor of FIG. 1(d). Opaque layer 9 may also be provided in the FIG. 1(d) embodiment if desired, over a portion of the window so as to shield the capacitor electrodes from a vehicle passenger's view. In the embodiment shown in FIG. 1(d), the electrodes 7 and 8 may be formed of a conductive silver frit or ITO printed or patterned directly on and contacting the surface of substrate 1. However, this invention is not so limited, and the electrodes 7 and 8 of one or more capacitors of the sensor may instead be formed and patterned from a metallic conductive IR reflecting layer (e.g., silver based layer) of a low-E coating 4 that is supported by the window.

FIG. 1(e) is a cross sectional view of another example embodiment of this invention, which may use the system of FIG. 1(a) and one or more of the embodiments of FIGS. 2-12. In the FIG. 1(e) embodiment, the vehicle window (e.g., laminated windshield) includes glass sheets 1 and 2 laminated together via polymer based interlayer 3, and optionally includes a low-E coating 4 on either substrate 1 or substrate 2. The FIG. 1(e) embodiment differs from the FIG. 1(b) embodiment in that the electrodes 7, 8 of the capacitor (e.g., C1, C2, C3 or C4) are provided on the major surface of the exterior glass substrate 2 that is closest to the vehicle interior. The capacitor electrodes 7, 8 may contact the polymer interlayer 3 in this embodiment, in certain example instances. The capacitor (e.g., C1, C2, C3 or C4) shown in FIG. 1(e) is designed such that it is affected by a rain droplet (or other material) on the exterior surface of the window because the electric field Es of the capacitor extends to or beyond the exterior surface of the window as shown in FIG. 1(e) and thus can interact with the rain droplet or other material on the window's exterior surface. Each of the capacitors C1-C4 of the sensor array is formed in a manner similar to that shown for the capacitor of FIG. 1(e). Opaque layer 9 may also be provided in the FIG. 1(e) embodiment if desired, over a portion of the window so as to shield the capacitor electrodes from the view of a vehicle passengers(s).

FIG. 1(f) is a cross sectional view of another example embodiment of this invention, which may use the system of FIG. 1(a) and one or more of the embodiments of FIGS. 2-12. In the FIG. 1(f) embodiment, the vehicle window (e.g., laminated windshield) includes glass sheets 1 and 2 laminated together via polymer based interlayer 3, and optionally includes a low-E coating 4 on either substrate 1 or substrate 2. The FIG. 1(f) embodiment differs from the FIG. 1(b) embodiment in that the electrodes 7, 8 of the capacitor (e.g., C1, C2, C3 or C4) are provided on the major surface of the interior glass substrate 1 that is closest to the vehicle interior, via support member 12. The support member 12, located between the glass substrate 1 and the electrodes 7, 8, may be made of glass, silicon or the like. The capacitor (e.g., C1, C2, C3 or C4) shown in FIG. 1(e) is designed such that it is affected by a rain droplet (or other material) on the exterior surface of the window because the electric field Es of the capacitor extends to or beyond the exterior surface of the window as shown in FIG. 1(f) and thus can interact with the rain droplet or other material on the window's exterior surface. Each of the capacitors C1-C4 of the sensor array is formed in a manner similar to that shown for the capacitor of FIG. 1(f). Opaque layer 9 may also be provide in the FIG. 1(f) embodiment if desired, over a portion of the window so as to shield the capacitor electrodes 7, 8 from the view of a vehicle passengers(s).

FIG. 2B is a plan view of an example pattern for a quadrant capacitive array of fractal shaped capacitors C1-C4 for the capacitive sensor according to another example embodiment of this invention. The four capacitors shown in FIG. 2B are similar to those of FIG. 2A, except for the precise shapes thereof. The FIG. 2B capacitors may be used in any of the embodiments of FIGS. 1(a)-(f). The super-imposed dashed lines show the divisions into four distinct capacitors C1-C4. The outer line width may be about 2 mm, and the inner line width about 1 mm, in certain example embodiments.

FIG. 3 is an enlarged picture of another exemplary quadrant capacitive array of fractal shaped capacitors C1-C4 for the capacitive sensor according to another example embodiment of this invention. The four capacitors shown in FIG. 3 are similar to those of FIGS. 2A and 2B, except for the precise shapes thereof. The FIG. 3 fractal capacitors may be used in any of the embodiments of FIGS. 1(a)-(f). The superimposed lines show example division between capacitors C1-C4 in FIG. 3. It will be appreciated that some example embodiments may have capacitive arrays with as few as two capacitors. However, it is preferable to have at least four capacitors in certain example embodiments to pick up and derive nuances in perturbations.

The use of the fractal geometry for the sensing capacitors C1-C4 can be advantageous in reducing false readings due to EMI interference in certain example embodiments of this invention. In particular, fractals at high iterations help reduce EMI interference issues, because the Faraday cage or quasi-Faraday cage of the fractal at high iterations reduces EMI coupling thereby reducing adverse effects of EMI interference. Fractals at high iterations form quasi-Faraday cages.

In certain example embodiments of this invention, the readout electronics look at the interaction of rain and/or other perturbations on the window. In certain example embodiments, this process may be accomplished by sequentially charging capacitors, reading their data, quantizing that data, and/or erasing the charges.

FIG. 4 is a circuit diagram of a read-out circuit according to an example embodiment of this invention. The circuit of FIG. 4 may be made up of the electronics unit 12 and the capacitive sensor array 5 of FIG. 1. Any of the capacitors of FIGS. 1(b)-1(f), 2A, 2B, and/or 3 may be used as the capacitors C1-C4 of the circuit in FIG. 4. The FIG. 4 circuitry is used for a write clock pulse in readout electronics, in certain example embodiments of this invention. Transistors Q1, Q2, and Q7 are p-channel MOSFETs, with transistors Q1 and Q2 primarily being responsible for a write phase. Transistors Q5 and Q6 are n-channel MOSFETs.

Still referring to FIG. 4, during a write phase a write pulse Clk_(wr) is input to the gate of transistor Q7, which functions like a resistor or switch, charging one or more of the capacitors C1-C4 of the sensor capacitance C_(s). FIG. 6 includes certain signals used in the FIG. 4 circuit in the write cycle. In the write cycle, Transistor Q1 is in a saturated mode, since its gate and drain are connected, so that Q1 is on. Q4, Q5 and Q6 are turned off, and Q2 is on during the write mode. Transistors Q3 and Q4 are optional. When Q7 is turned on by the write pulse, we have a write cycle, and Vcc appears at Cs via A and charges one or more of the capacitors C1-C4 of the sensor capacitance Cs. V_(cc) may be a constant voltage, such as 5V, in certain example embodiments. One or more of the capacitors C1-C4 may be charged at a time during a write cycle. However, in certain example embodiments of this invention, the circuit charges and reads the capacitors C1, C2, C3 and C4, one at a time (e.g., see FIG. 6). Thus, during one write cycle, only one of the capacitors C1, C2, C3 or C4 is charged in certain example embodiments of this invention.

The above process described for the left side of the FIG. 4 circuit is essentially mirrored on the opposite or right side of the FIG. 4 circuit. As current flows through the left-side branch, current also flows at B through the right-side branch, and changes to C_(s) are mimicked, or substantially mimicked in internal mimicking capacitance C_(int). When Q7 is turned on, current also flows through Q2 (which is on) and charges C_(int) using Vcc. Thus, the charging of one of the capacitors C1-C4 is mimicked by the charging of capacitor C_(int). In other words, C_(int) is charged to the same degree, or substantially the same degree, as the capacitor (e.g., C1) being charged on the other side of the FIG. 4 circuit. The output voltage of the FIG. 4 circuit, Vout (or Vo), is based on C_(int) and is taken at or proximate an electrode of the capacitor C_(int) as shown in FIG. 4. An example formula reflecting Vout (or Vo) is shown at the bottom of FIG. 4. Accordingly, it will be appreciated that the output Vout (or Vo) of the FIG. 4-5 circuit is related to and based on the capacitors C1-C4 of the sensor Cs. More specifically, the output Vout of the FIG. 4-5 circuit is related to and indicative of the status of capacitors C1-C4 and the effects on those capacitors of moisture on the exterior window surface, even though Vout is not taken directly from capacitors C1-C4. In particular, Vout (or Vo) is read out during the write cycle, due to the write pulse shown in FIG. 4 (see also FIG. 6). In the formula at the bottom of FIG. 4 for Vout, W1 is for Q1, W2 is for Q2, L1 is for Q1, L2 is for Q2, where W is transistor channel width, and L is transistor channel length; and VT is a threshold voltage of each MOSFET. It is noted that in alternative embodiments of this invention, the output Vout of the circuit may be taken directly (instead of indirectly via C_(int)) from the sensing capacitors C1-C4.

Transistors Q3 and Q4 are optional. In certain example embodiments, these transistors may be at low voltages (e.g., off) during the write phase, and on during the erase phase.

The output signal Vout (or Vo) of the FIG. 4 (and FIG. 5) circuit is sigma-delta modulated in certain example embodiments of this invention. Sigma-delta modulators, which can be used in a sigma-delta digital-to-analog converter (DAC), can provide a degree of shaping or filtering of quantization noise which may be present. Example sigma-delta modulators that may be used are described in U.S. Pat. Nos. 6,975,257, 6,972,704, 6,967,608, and 6,980,144, the disclosures of which are hereby incorporated herein by reference. In certain examples of sigma-delta conversion, oversampling, noise shaping and/or decimation filtering may be brought to bear. Example advantages of sigma-delta modulation include one or more of: (i) analog anti-aliasing filter requirements are less complex and thus may be cheaper than certain example nyquist based systems; (ii) sample and hold circuitry may be used due to the high input sampling rate and low precision A/D conversion; (iii) since digital filtering stage(s) may reside behind the A/D conversion, noise injected during the conversion process such as power-supply ripple, voltage reference noise and noise in the A/D converter itself may be controlled; (iv) since the sigma-delta converter may be essentially linear it may not suffer from appreciable differential non-linearity and/or background noise level(s) may be independent of input signal level. Improved S/N ratios may be realized.

FIG. 25 which is a simplified version of a sigma-delta modulator system according to an example embodiment of this invention, for modulating and/or converting the output signal Vout (or Vo) of the FIG. 4 (and FIG. 5) circuit. In FIG. 25, a write pulse (see pulse at the bottom of FIG. 25), is used to charge the sensing capacitor (C1, C2, C3 or C4) as explained above with respect to FIG. 5. The square wave excitation (e.g., for writing and/or erasing cycles) is used on the sensing capacitor to charge and discharge it. This process is mirrored or mimicked, for C_(int) as explained herein. The output signal Vout (or Vo) of the FIG. 4 circuit is sigma-delta modulated by sigma-delta modulator 60. The modulator 60 make take the form of a hardware circuit, firmware, and/or software in different example embodiments of this invention. Clock pulses 62 from a clock are input to the modulator 60, which trigger the latch of a quantizer of the modulator 60. After the output signal Vout (or Vo) are sigma-delta modulated by modulator 60, the modulated signals 64 are forwarded to an optional digital filter 66 (e.g., lowpass filter or the like). Digital filter 66 processes the sigma-delta modulator digital output 64, which is a stream of 0s and 1s. The data is then scaled appropriately using calibration coefficient(s). The filtered data 68 is then read through a serial interface 69 or the like and sent to a computer which does the correlation calculations for chunks of data packets. Thus, the data from the interface 69 is then correlated (e.g., autocorrelated and/or cross-correlated) as explained herein. FIG. 26 is similar to FIG. 25, except that FIG. 26 illustrates an array of sensing capacitors C1-C4 which are multiplexed via a multiplexer.

FIG. 27 is a block diagram illustrating an example of sigma-delta modulation which may be performed in the modulator 60 of FIGS. 25-26. Again, this modulation may be performed by circuitry, firmware and/or software in different example embodiments of this invention. The analog output signal Vout (or Vo) of the FIG. 4 (and FIG. 5) circuit is received by a summer 70 of the sigma-delta modulator 60. Summer 70 receives the analog Vout (or Vo) signal as well as a feedback signal from a feedback loop 71 of the modulator 60. The output of summer 70 is received by integrator 72 whose output is received by a quantizer 74 such as a one bit quantizer. The digital output 64 is then filtered 66 as explained above, and so forth. The sigma-delta modulation is advantageous in that it provides oversampling and allows noise such as EMI to be treated and its adverse effects reduced. In particular, the noise is spread by the sigma-delta modulation out over the frequency band so that the signal-to-noise (S/N) ratio can be improved.

Referring back to FIG. 4, each capacitor (C1, C2, C3, C4) is discharged before charging the next, in certain example embodiments of this invention. The process of discharging each capacitor is described in connection with the erase pulse, with respect to FIGS. 5-6.

FIG. 5 is a circuit diagram of the FIG. 4 circuit, with respect to an erase cycle. During an erase cycle, a previously charged capacitor (C1, C2, C3 or C4) is discharged before the next write cycle. FIG. 6 includes example signals used during the erase cycle(s). No reading is performed during the erase phase, in certain example instances. During an erase cycle or phase, Q7 is turned off (the write pulse Clk_(Wr) is not present), and transistors Q5 and Q6 are turned on by an erase pulse Clk_(Er) (see also FIG. 6). Thus, the capacitor (C1, C2, C3 and/or C4) discharges to ground (e.g., V=0) or virtual ground (VG), as does C_(int). Again, C_(int) mimics the capacitance of the sensor Cs. Once the capacitances Cs and C_(int) have been connected to ground and discharged, the erase pulse and cycle ends. Then, the next capacitor (C1, C2, C3 or C4) in the sequence can be prepared, charged, and read.

Still referring to FIG. 5, in certain example embodiments of this invention, during the erase cycle, the erase pulse Clk_(Er) causes the capacitor (C1, C2, C3 and/or C4) and thus also the mimicking capacitance C_(int) to discharge to ground (e.g., a fixed potential such as V=0) (see the conventional ground symbol in FIG. 5). However, in other example embodiments of this invention, it has been found that a fixed ground can lead to certain problems. Thus, in such other example embodiments of this invention, during the erase cycle the erase pulse Clk_(Er) causes the capacitor (C1, C2, C3 and/or C4) and thus also the mimicking capacitance C_(int) to discharge to a virtual ground VG that is floating (see VG and the ground symbol in FIG. 5). Stated another way, an electrode of each of capacitors C1-C4 is floating. It may be at a floating or reference potential/voltage. It has been found that a floating or virtual ground can be highly advantageous in certain example embodiments of this invention (e.g., a floating ground and/or capacitor electrode(s) can lead to a significant reduction in EMI interference problems). For example, such a floating or virtual ground may help reduce the chance of the sensor system being tricked by EMI interference. In this respect, reference is made to FIGS. 28(a) and 28(b) (along with FIG. 5).

In FIGS. 28(a)-(b), reference numerals 7 and 8 refer to the electrodes of a capacitor (e.g., C1, C2, C3 or C4). In these figures, “q” refers to charge and Φ refers to potential (Φ1 is different than Φ2). In FIG. 28(a) the capacitor (e.g., C1) is grounded at a fixed potential such as 0 volts (the charge at grounded electrode 7 is fixed at +q). In this respect, when the charge at grounded electrode 7 is fixed at +q, when one brings an external body E_(B) (e.g., human finger with a higher dielectric constant) into a sensing area of the capacitor (e.g., touching the front surface of the windshield over the capacitor) this external body induces a change in charge −Δq and the other electrode 8 which is not fixed changes from a charge of −q to a charge of −q+Δq in an attempt to balance charge. Thus, if one were to ground the capacitor at a fixed potential such as 0 volts, and read an output voltage of the capacitor, one would read charge changes caused by Δq which is not needed, and this may lead to false readings. Comparing FIGS. 28(a) and 28(b), FIG. 28(b) illustrates an advantage of causing an electrode 7 of the sensing capacitor (e.g., any of C1-C4) to be floating (e.g., at a floating or virtual ground). In FIG. 28(b), the charge q at electrode 7 is not fixed. E.g., the charge at electrode 7 changes from +q′to +q″ when the external body comes into contact with the windshield at a sensing area of the capacitor, thereby indicating the floating nature of the electrode. In FIG. 28(b), when the external body (e.g., human finger) is applied to the windshield over the capacitor sensing area the free charges on both electrodes 7 and 8 of the capacitor change. Thus, the adverse effect of Δq is eliminated or reduced by using the floating or virtual ground VG (electrode 7 is floating). In particular, when electrode 7 is floating as in FIG. 28(b), the external body (E_(B)) does not adversely affect summation of charge because adding the charges (+q″ and −q″) of the electrodes 7 and 8 when the external body is present gives zero or substantially zero. False readings due to EMI interference can also be reduced by using this floating feature. Thus, in certain example embodiments, the floating nature may allow the absolute values of the charges q at capacitor electrodes 7 and 8 to be the same or substantially the same even when the external body is present since the electrode 7 is floating and is not fixed at ground. This is one example reason why it may be advantageous to cause the electrodes 7 of the capacitors C1-C4 to be floating, or be at a virtual ground VG as shown in FIG. 5. Thus, referring to FIGS. 5 and 28, the sensing capacitors C1-C4 are floating and both electrodes thereof are isolated from ground.

FIG. 6 is an exemplary timing diagram of signals applied to or read out from the FIG. 4-5 circuit during the write and erase modes/cycles. As noted above, the capacitors (C1-C4) are sequentially charged, read, quantized, and erased. FIG. 6 shows a clock write (Clk_(Wr)) and erase (Clk_(Er)) pulse for each capacitor C1-C4, in sequence. Then, voltages are quantized and output. Variable output voltage Vo1-Vo4 correspond to capacitors C1-C4 respectively, and thus C_(int). It is noted that the output signals Vo1-Vo4 in FIG. 6 are taken at V_(out) (or Vo) in FIGS. 4-5. Moreover, in FIG. 6, the output signals Vo are read or analyzed (e.g., for autocorrelation and/or cross-correlation) at the peak read areas (see “Read” in FIG. 6) of the output signals where the output signals are substantially stabilized and/or the capacitor saturated. In particular, the output signal V_(out) (or Vo) in FIG. 6 for a particular capacitor (C1) is read in the “read area” after the end of the write pulse (Clk_(Wr)) for that capacitor, and before and/or up to the beginning of the erase pulse (Clk_(Er)) for that capacitor.

Still referring to FIG. 6, for example, a drop of water on the exterior surface of a windshield will affect the magnitude of the output signal(s) V_(out) (or Vo). For instance, a water drop over the area of a given capacitor (e.g., C1) will cause the level of the output signal(s) V_(out) (or Vo) for that capacitor in the “read” area of the signal to be higher compared to a situation where no such drop was present. The exact magnitude or level depends on the size of the water drop. With increasing water amounts, the magnitude of the signal at the “read” area gets higher because the dielectric constant of water is higher than that of glass and/or air and this causes the capacitance to increase. In a similar manner, if no water drop is present on the windshield over the area of a given capacitor (e.g., C1) then this will cause the level of the output signal(s) V_(out) (or Vo) for that capacitor in the “read” area of the output signal to be lower compared to a situation where a drop was present.

The signals (e.g., from the capacitor(s)) may be converted from analog-to-digital via a sigma-delta modulation scheme or the like, which may be implemented at the software level or in any other suitable manner such as via hardware. The principle behind sigma-delta architecture is to make rough evaluations of the signal, to measure the error, integrate it, and then compensate for that error. Data may be oversampled at a given rate of at least 32 kHz, e.g., more preferably 64 kHz, though it will be appreciated that other sampling rates may be used. The course quantization can be recovered by the sigma-delta modulation scheme to produce a simple binary 0 or 1 output, corresponding to on and off, respectively. Thus, the sigma-delta modulation scheme may be used to reduce noise (e.g., at the tail of the signal) and produce a digital output stream (e.g., 1s and 0s).

Before discussing the detailed operation of and example mathematics behind an example sensor algorithm, an overview of the states in which the sensor and/or wipers can take will be given in connection with FIG. 7, which is an exemplary state diagram showing how autocorrelation and cross-correlation data may be used to control vehicle wipers. The system begins in Start/Initialization State S702. In this state, all buffers are cleared in certain example instances. Based on the inputs of capacitors C₁, C₂, . . . , C_(n), analog-to-digital conversion of the signals from the respective inputs is accomplished via sigma-delta modulation. Data is read for the plurality of channels over time period T. Operating Mode Selector State S704 functions as a switch to select between the manual or automatic wiper mode. If Operating Mode Selector State S704 indicates that manual mode is selected, then in Manual Mode State S706 an auto mode may be disabled and a pre-existing manual mode enabled. Then, the system returns to Start/Initialization State S702. However, if Operating Mode Selector State S704 indicates that auto mode is selected, the automatic wiper mode is enabled in Auto Mode State S708.

In Autocorrelator Engine State S710, at least three computations are performed. First, a normalized autocorrelation is calculated for each signal input of the capacitive array. Second, the gradient of the autocorrelation is calculated. Third, the difference between the signal input and a reference non-disturbed signal (A1) may be calculated. This information is passed to Is Raining? State S712, in which at least three conditions are checked to determine whether it is likely that it is raining, there is moisture on the windshield, etc. Likely indications of rain are that the gradient of the autocorrelation is greater than 1, all autocorrelation values are positive, and/or Δ₁ is greater than some pre-defined threshold value t1. If these conditions are not met, the system moves to Park Wipers/Stop Motor State S714, where wipers are parked (if they are moving) or not actuated, and the motor is stopped (if it is engaged), and the system is returned to Start/Initialization State S702.

On the other hand, if all conditions are met (e.g., it is likely that there is an interaction of water, moisture or some other perturbation on the glass, etc.), the system moves to Lowest Speed State S716, in which the wiper motor is activated at the lowest speed available. In Cross-Correlator Engine State S718, the cross-correlation between the input signals from the capacitors is calculated. The cross-correlation curve shape is determined, and the symmetry of the two sides of the cross-correlation curve are checked for symmetry. As will be described below, these checks help, for example, to determine the type of perturbation (e.g., light rain, heavy rain, fog, snow, etc.) hitting the window (e.g., windshield). In Rain Degree Assessment State S720, the “degree of rain” (e.g., heavy, light, etc.) is determined. Based on this determination, the wiper motor is activated at the appropriate speed in Speed Selector State S722. Lastly, the system is returned to Start/Initialization State S702 to determine whether there is any change in conditions outside the car.

The steps performed by the rain sensor will be described in greater detail in connection with FIG. 8, which is an exemplary flowchart showing how autocorrelation and cross-correlation data can be used to control wipers in certain example embodiments of this invention. In FIG. 8, in step S800 buffers are cleared, and data outputted from the FIG. 4-5 circuit (e.g., from C_(int), or from capacitors C1-C4) is sigma-delta modulated, and is read in S802.

The algorithm for determining whether to engage wipers and, if so, the speed at which to engage wipers begins by autocorrelating the sigma-delta modulated data in step S804. Autocorrelation may be used for analyzing functions or series of values, such as time domain signals. An autocorrelation is the cross-correlation of a signal with itself. Autocorrelation is used for finding repeating or substantially repeating patterns in a signal, such as, for example, determining the presence of a periodic signal buried under noise, identifying the fundamental frequency of a signal that does not actually contain that frequency component but implies within it with many harmonic frequencies, etc. Cross-correlation is a measure of the similarity of two signals, and it is used to find features in an unknown signal by comparing it to a known one; in other words it may be used to perform signal fingerprinting in certain instances. Cross-correlation is a function of the relative time between the signals. In certain example embodiments of this invention, digital signals from any two capacitors (e.g., C1 and C2) are cross-correlated, in close spatial proximity, and the system looks for any degree of correlation at time lags other than a time lag of zero. This spatio-temporal cross-correlation allows the system to extract patterns in how the falling rain is electrically projecting itself over the sensor array. As an example, the system may take the case of rain drops moving over one capacitor C1 at a time t0 and the same drop “ringing” another capacitor C4 (spatially separated by distance L from C1). If the drop moves at an average speed Vi, the time (t0+T), where T=L/Vi, the cross-correlation function will have another extremum or kink. The normalized magnitude of this extremum value may allow the system to determine the degree of rain falling on the sensor.

Each capacitor C1-C4 has an autocorrelation function associated with the digitized Vout resulting from the readout thereof (or the corresponding readout of C_(int)). In example embodiments, the autocorrelation function depends on time difference, rather than on actual time. Computing autocorrelations is beneficial because it allows, for example, the deduction of the fundamental frequency irrespective of phase. Autocorrelations are advantageous over other methods, such as Fourier transforms (which may also be used in certain example embodiments of this invention) which provide information about the underlying harmonics only. Thus, the use of autocorrelation of the readouts from capacitors C1-C4 (which as explained above, includes the corresponding readouts from mimicking C_(int)) can be used to detect and distinguish between beads of water, dirt, dust, droplets, downpour, etc.

It is noted that herein data from C_(int) is considered to be data from the capacitors C1-C4 because the capacitance C_(int) mimics or substantially mimics the capacitances C1-C4 as explained above. Thus, when we talk about receiving data from the capacitors (e.g., C1-C4), this covers and includes receiving data from capacitance C_(int). In other words, the output from the FIG. 4-5 circuit is considered to be from the capacitors C1-C4, even though it is not taken directly therefrom.

Rain, as a function of time, may be represented by the following formula: ${b\left( {\overset{->}{r},t} \right)} = \left\{ \begin{matrix} 1 & {{rain}\quad{projects}\quad{electrically}} \\ 0 & {otherwise} \end{matrix} \right.$ Essentially, b takes on a binary value indicating whether it is raining (1), or not (0). It will be appreciated that b is at least two bits, and that for sigma-delta modulation 24-bits may be used in certain example embodiments. It also will be appreciated that a scale could be introduced, potentially to capture more data related to the voltages in the capacitors C1-C4 (or C_(int)).

At the end of a sampling cycle L, for example, the output from the FIG. 4-5 circuit, e.g., from the array of four capacitors C1-C4 (or via C_(int)), ranges from 0000 to 1111 in certain example embodiments, using binary digital data. A single bit turned on can initiate a single wipe in certain example instances. In the case when all bits are off (0000) or all bits are on (1111), then no wipes may be initiated in certain example instances, because likely there is nothing on the windshield, the car is completely submerged, etc., since all capacitors in the array would be reading the same which is not consistent with rain falling on a window. Thus, the most probable events where wipers will be needed are those in the range of 0001 to 1110 (i.e., when the output from all capacitors in the array is not the same). When the data falls in this range, or even if it does not fall within this range, correlation functions (auto and/or cross correlation functions) may be performed using the following integral. It will be appreciated that the integral below can be rewritten in other forms, such as, for example, as a summation. The correlations between two drops over a large time period may be computed according to the following formula: ${R_{b}\left( {r_{1},{t;r_{2}},t_{2}} \right)} = {\frac{1}{L}{\int_{0}^{L}{{b\left( {r_{1},{t_{1} + t}} \right)}{b\left( {r_{2},{t_{2} + t}} \right)}{\mathbb{d}t}}}}$ ${R_{b}\left( {r_{1},{t;r_{2}},t_{2}} \right)} = {R_{b}\left( {{\Delta\quad\overset{->}{r}},{\Delta\quad t}} \right)}$ where R_(b) is the correlation of a binary event, given as a function of the resistances r_(i) at given times t_(i).; and L is a large sampling period during which a burst of data is captured. In certain example embodiments, the sampling period L may be from about 10 to 100 ms, and more preferably from about 20-30 ms, which corresponds approximately to the frequency an average human eye can discern. R_(b) also is equal to a function of the correlation of the changes in resistances across capacitors Δ{right arrow over (r)} and the change in time. When Δ{right arrow over (r)}=0, the autocorrelation value is determined since data from the same capacitor is being analyzed, and when Δ{right arrow over (r)}≠0, cross-correlations are computed since correlation is performed on data from different capacitors.

These functions are subject to several example constraints and underlying assumptions. First, Δ{right arrow over (r)}=V{right arrow over (i)}Δt. This constraint essentially means that a drop of water or the like is moving at a given time scale. Second, b({right arrow over (r)}+V{right arrow over (i)}Δt,t+Δt)=b({right arrow over (r)},t). This constraint mimics or substantially mimics what happens when drops of water or the like move from one capacitor to another. Thus, the correlation functions might be thought of as discrete steps p in space and Tin time. This feature may be mathematically represented as the following equation: R _(b)(m{right arrow over (p)},nT)≡R(V{right arrow over (i)}Δt,Δt) Essentially, the left-hand side of the equation establishes a theoretical grid in space and time across which a drop of water or the like moves. For example, FIG. 9 is an exemplary stylized view of how a rain droplet might travel across a windshield. FIG. 9 shows a rain droplet moving across a windshield on the X-Z plane during an initial time period (t=0) and some late quantum of time (t=T). The assumption that drop distribution is uniform over space and time allows the creation of a binary field caused by rain that is in a wide sense stationary. The system also assumes that the temporal correlation between preferred pixels in the same neighborhood is high in the direction of rain. Lastly, the degree of autocorrelation and cross-correlation in time quantifies rain fall and other disturbances.

It will be appreciated that in certain example embodiments, computational time can be saved because of the nature of correlation matrices and the nature of rainfall. For example, correlation matrices may be symmetrical in certain example instances. Additionally, as another example, because rain tends to fall down from the sky and move up along a windshield, it may be sufficient to compare only capacitors that are disposed vertically relative to one another in cross-correlation, while ignoring horizontally adjacent capacitors.

It is noted that while binary data is used in certain example embodiments of this invention, this invention may also utilized grey scale data in certain example instances with respect to outputs from the circuit of FIGS. 4-5, or from similar or other suitable circuit(s).

After the autocorrelation has been performed in step S804 (e.g., using the equation(s) discussed above, or some other suitable correlation equation(s)), one or more checks may be performed to enhance the accuracy of the system. Examples of such checks (e.g., if the autocorrelated data Rxx has negative values, if a gradient is greater than one, and/or if the shape of a Rxx curve is different or substantially different from a normalized non-disturbed autocorrelation data stored in memory) are listed in the bottom part of the box for step S804 in FIG. 8. One, two or all three of these checks may be performed.

For example, one check of the autocorrelation data in step S806 may be to determine whether the autocorrelated data from one or more of the capacitor(s) (C1, C2, C3 and/or C4; or via mimicking C_(int)) comprises negative values. For instance, when the autocorrelated data has negative value(s), then the system or method may indicate that it is not raining, may park the wipers, and/or may not actuate windshield wipers (see step S808). This check is for determining, for example, whether a detected disturbance is actually rain. In this respect, FIG. 10 is a graph plotting example experimentally-obtained maximum values of non-normalized autocorrelations for different disturbances. FIG. 10 illustrates that water signals are greater than non-disturbed signals and are positive, and that external interferences such as electromagnetic waves from CB radios and human hand touching of a window tend to be below the no-disturbance levels and may be negative. Thus, to eliminate or reduce false detections due to external disturbances such as, for example, a human hand touching the window, radio signal interference, etc., any signal with negative autocorrelation values is considered a “no-rain” event. It will be appreciated that some example embodiments may consider negative autocorrelation values. Other example embodiments may take other measures to eliminate or reduce false detections due to external interferences by, for example, comparing gradients (e.g., any curve lower or less than the no-disturbance curve/plot of FIG. 10 may be considered a “no-rain” event), shielding capacitors, etc.

A second example check of the autocorrelation data is to check whether a gradient of an autocorrelation curve associated with the autocorrelated data is greater than one; and if not then the system or method may indicate that it is not raining, park the wipers and/or not actuate wipers of the vehicle (see step S808). In this check, the gradient of the normalized autocorrelation of the disturbance is checked. The gradient of the normalized autocorrelation of a non-disturbed signal is close to 1. Measuring the gradient is beneficial because it is not affected by temperature change. Thus, the rain sensor may be substantially immune to false reads due to temperature changes in certain example embodiments of this invention. In certain example instances, gradients less than 1 (or some other predetermined value) may be considered no-rain events.

A third example check of the autocorrelation data is to determine whether there is a match or substantial match between an autocorrelation curve associated with the autocorrelated data and one or more predetermined autocorrelation curve(s) stored in a database and/or memory. When the shape of the autocorrelation curve associated with the autocorrelated data from the FIG. 4-5 circuit is different or substantially different from an autocorrelation curve relating to normalized non-disturbed autocorrelation data, this may be considered a no-rain event and it may be indicated that it is not raining, wipers may be parked, and/or wipers may be not actuated (see step S808). However, when there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data from the FIG. 4-5 circuit and a predetermined autocorrelation curve associated with moisture such as rain, then it may be indicated that it is raining, wipers may actuated, or kept moving.

In this regard, the shape of the autocorrelation curve may be used to reduce false wipes and/or false detections. In particular, the normalized autocorrelation of a non-disturbed signal is used as a reference. Then, the normalized autocorrelation of each signal captured from the FIG. 4-5 circuit is compared to the reference to identify the closest fingerprint in certain example instances. Generally, the more water present in the sensing area, the larger the difference between the reference signal and the observed signal. In this way, correlation snapshots can be compared to reference snapshots of well-known events such as the presence of rain, dirt, no-disturbance, ice, and so forth. In general, correlation snapshots may be normalized, though the invention is not so limited. Correlation snapshots preferably plot r-values versus quantums of time over a discrete time interval in certain example embodiments of this invention.

In certain example embodiments, when there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data from the FIG. 4-5 circuit and a predetermined autocorrelation curve associated with a non-moisture substance such as dirt, then this may be considered a no-rain event and it may be indicated that it is not raining, wipers may parked and/or not actuated (see step S808).

Thus, it will be appreciated that the shape of the autocorrelation curve resulting from the data output from the FIG. 4-5 circuit (from the capacitors C1-C4, or via C_(int)) may be used to reduce false wipes as a third condition. For instance, a normalized autocorrelation curve of a non-disturbed signal may be used as a reference. Then, the normalized autocorrelation of each signal captured from the FIG. 4-5 circuit is compared to the reference to identify the closest fingerprint. Generally, the more water present in the sensing area, the larger the difference between the reference signal and the observed/detected signal. In this way, correlation snapshots can be compared to reference snapshots of well-known events. In general, correlation snapshots preferably are normalized, though the invention is not so limited. Correlation snapshots preferably plot r-values versus quantums of time over a discrete time interval.

A potential problem with capacitive rain sensors is that rapid temperature changes (e.g., due to the radiation absorbing black frit used to cosmetically hide the sensor pattern) change the dielectric “constant” (permittivity) of the glass. This is then registered as a capacitance change and may erroneously be interpreted as a rain signal. However, according to certain example embodiments of this invention, a normalized autocorrelation function is unchanged, or substantially unchanged, for different temperatures even though there may be differences for the non-normalized autocorrelation functions for the different temperatures. Thus, in certain example embodiments of this invention, the sensing system is unaffected or substantially unaffected by temperature changes.

In addition, extremely slow accumulation of water like ultra-fine mist can slowly build up to a level that triggers sensors based on Nyquist rate converters. In the time of observation that concerns human vision (e.g., 30-60 Hz), the autocorrelation function in certain example embodiments of this invention is able to discriminate between the ultra-slow accumulation of dew or condensation and normal mist and rain.

FIGS. 11A-11D provide sample experimentally-obtained correlation snapshots. These correlation snapshots, or fingerprints of an event, can be stored as reference fingerprints or correlation curves. Observed/detected correlation snapshots (e.g., autocorrelation curves) can be compared to these reference fingerprints to determine the type of event occurring. For instance, FIG. 11A is an experimentally-obtained autocorrelation snapshot indicative of heavy rain. FIG. 11B is an experimentally-obtained autocorrelation snapshot indicative of a light mist. FIG. 11C is an experimentally-obtained autocorrelation snapshot indicative of CB radio interference. FIG. 11D is an experimentally-obtained autocorrelation snapshot indicative of a grounded body with a voltage. It will be appreciated that these fingerprints are provided as non-limiting examples and reflect experimentally-obtained data. Actual events may differ in various characteristics. Thus, in certain example embodiments of this invention, when it is determined that there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data from the FIG. 4-5 circuit and a predetermined non-moisture autocorrelation curve such as that of FIG. 11C or FIG. 11D, then this may be considered a no-rain event and it may be indicated that it is not raining, wipers may parked and/or not actuated (see step S808). However, in certain example embodiments of this invention, when it is determined that there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data from the FIG. 4-5 circuit and a predetermined moisture-related autocorrelation curve such as that of FIG. 11A or FIG. 11B, then this may be considered a rain event and it may be indicated that it is raining, wipers may actuated and/or kept moving. In addition to the predetermined autocorrelation curves of FIGS. 11A-11D, other reference fingerprints may be stored and/or compared with observed correlation snapshots in other example embodiments of this invention.

Turning back to FIG. 8, in step S806 it is determined whether each of the three conditions set forth in the bottom portion of the S804 box is met. In particular, it is determined in S806 whether each of the following is met: (a) the autocorrelated data has no negative values; (b) a gradient of an autocorrelation curve associated with said autocorrelated data is greater than a predetermined value such as one; and (c) the shape of the autocorrelation curve associated with the autocorrelated data from the FIG. 4-5 circuit is different than a predetermined autocorrelation curve associated with non-disturbed autocorrelation data. If they are not all met, this is an indication of a non-rain event and the process moves to step S808 where the vehicle wiper(s) are parked (if they were moving) or are kept off, and begins initialization S800 again. However, if all of these requirements are met in S806, then the process moves to S810 and the vehicle's wipers (e.g., windshield wipers) are activated at their lowest speed.

For purposes of example only, and understanding, FIG. 13 illustrates an example of autocorrelation. In FIG. 13, the values from (or relating to) sensing capacitor C1 are, at sequential times −t2, −t1, t0, t1, t2 and t3 are 0, 0, 1, 1, 0 and 0, respectively. Autocorrelation for time 0 (aco) is determined by multiplying the values relating to C1 in a non-offset manner, and then adding or summing the results. It can be seen in FIG. 13 that aco is equal to 2 in this instance. Thus, on the autocorrelation graph at the bottom of FIG. 13, an entry in the graph at time 0 is made for an autocorrelation value of 2. Note that the autocorrelation graph at the bottom of FIG. 13 is similar, but simpler, that the autocorrelation graph in FIG. 10 and the autocorrelation values may be obtained for FIG. 10 in a like manner. Next, still referring to FIG. 13, autocorrelation is performed using the capacitance values relating to C1 for the next point in time to obtain autocorrelation value ac1. This next autocorrelation value (ac1) is obtained by shifting the bottom row sequence of values for C1 relative to the top row as shown in FIG. 13, and then multiplying the values in the rows which line up with each other and summing the results. FIG. 13 illustrates that ac1 is equal to 1 for time 1. Thus, this autocorrelation value of 1 for time t1 may be entered in the graph at the bottom of FIG. 13 and a line is drawn between the two entered data points for purposes of example and understanding. The, for the next time value (or lag), the bottom row is again shifted another segment over relative to the top row and the process repeated, and so forth. It can be seen that the autocorrelation plots in FIG. 10 may be obtained in a similar manner. In FIG. 13, it will be appreciated that cross-correlation may be performed by replacing the C1-related values in the bottom row with values from or related to another capacitor such as C2 (or C3 or C4).

Examining autocorrelation and/or cross-correlation also can help distinguish between, for example, light rain and heavy rain. For example, if only the autocorrelation in time is high (and crosscorrelation is low), then there probably is only light rain. FIG. 12A is an exemplary correlation matrix showing light rain. Of note in FIG. 12A is that the correlations between C1 and C1, C2 and C2, C3 and C3, and C4 and C4 (these are autocorrelations) over a given time period are high, while the rest of the correlations (the cross-correlations) are low. By hypothesis and confirmed experimental data, a matrix of this sort would indicate a light rain.

On the other hand, if both autocorrelation and cross-correlation in time between capacitor signals are high, there is probably fast rain. FIG. 12B is an exemplary correlation matrix showing heavy rain. In FIG. 12B, not only are the autocorrelations of individual capacitors high (i.e., the autocorrelations are the correlations between C1 and C1, C2 and C2, C3 and C3, and C4 and C4), cross-correlations between different capacitors also are generally high (the correlations in FIG. 12B going diagonally from the upper-left to the bottom-right are the autocorrelations, and the rest are the cross-correlations). By hypothesis and confirmed experimental data, a matrix of this sort would indicate a fast rain. The degree of cross-correlation can be quantized to determine the relative speed of the rain. This data can, in turn, be used to trigger various wiper speeds, as appropriate for the speed of the rain. For instance, the more cross correlations that are high, the higher the wiper speed to be used.

More systematically, in step S812, cross-correlations are computed (correlations between data relating to different capacitors), and the two sides of the cross-correlation curve are used to determine a symmetry level L. If the symmetry level is lower than a predefined threshold t_(min), step S814 directs the system to step S816 where wipers are activated at the lowest speed, and the system is returned to initialization step S800. If the symmetry level is greater than t_(min) but less than an arbitrary value t, step S818 directs the system to step S820 where wipers are activated at a faster or medium speed, and the system is returned to initialization step S800. It will be appreciated that a plurality of arbitrary values t_(i) may be specified, and a symmetry level falling between t_(i) and t_(i+1) will activate an appropriate corresponding wiper speed and then return the system to initialization step S800. Finally, in step S822, if the symmetry level is above a predefined level t_(max), step S822 directs the system to step S824 where wipers are activated at the highest speed, and the system is returned to initialization step S800. Thus, correlations from the data output from the FIG. 4-5 circuit can be used to adjust wiper speed. In certain example embodiments, the more cross correlations that are high, the higher the wiper speed to be used due to the likelihood of heavier rain.

For purposes of example and understanding, FIGS. 14-24 illustrate examples of cross-correlation performed according to certain example embodiments of this invention. FIG. 14 sets forth cross-correlation data in certain example instances, whereas FIGS. 15-24 illustrate cross-correlation graphs of certain of the data from FIG. 14 where rain is detected. In FIGS. 15-24, each lag on the horizontal axis is one microsecond (1 μs) for purposes of example, and sampling was performed every one microsecond. As explained above with respect to FIG. 13, in FIGS. 15-24 at time=0 (lag 0), there is no shift in time of the values from the different capacitors being correlated. FIG. 14 illustrates that when rain was present (see signals S1-S5 and W1-W5), the delta signals relating to autocorrelation were high. FIGS. 15-24 are cross-correlation plots relating to these signals. It is helpful to look for symmetry between the plots on the left and right hand sides of each of FIGS. 15-24 (one side of zero is compared to the other side of zero). Generally speaking, if there is symmetry about the zero lag axis, there is not much cross-correlation which indicates that the detected rain is not very hard. However, if there is asymmetry about the zero lag axis, then this means more cross-correlation and indicates that the rain is hard or harder. For example, note the asymmetry in FIGS. 18, 19 and 23 about the zero lag axis due to the bumps or valleys on one or both sides. More cross-correlation indicates that the rain drops are moving from one capacitor's sensing area to another capacitor's sensing area. In this respect, each interaction of a rain drop and the surface of a windshield has its own correlation signature in the time domain. High cross-correlation indicates that the same drop is being detected at different capacitors, at different points in time (e.g., see FIG. 9 also). It is noted that the lower case “t” in FIG. 9 is the same as the lags axis in FIGS. 15-24.

Thus, it will be appreciated that certain example embodiments of this invention provide a moisture sensor (e.g., rain sensor) that can detect rain or other material on a vehicle window or other type of window or sheet/surface, without the need for a reference capacitor. Spatial temporal correlation may be used. All capacitors, or a plurality of capacitors, in the sensing array may be identical or substantially identical in shape in certain example embodiments. For purposes of example, at a given point in time (e.g., t1), the system may compare C1-relates values with C2 related values, and/or other capacitor related values. For this time t1, the system may also compare C1-related values with itself (autocorrelation), and may also compare autocorrelation for C1 with autocorrelation for C2 and/or other sensing capacitor(s).

It is noted that while capacitors C1-C4 are preferred for the sensing devices, it is possible that other types of sensing devices may instead or additionally be used on the window.

While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. 

1. A method of sensing the presence of moisture on a vehicle window, the method comprising: receiving data relating to at least two capacitors supported by the vehicle window; autocorrelating the data relating to each capacitor to obtain autocorrelated data; and determining, based at least on said autocorrelated data, whether moisture is present on an exterior surface of the vehicle window.
 2. The method of claim 1, further comprising: cross-correlating the data relating to the two capacitors so as to correlate data relating to different capacitors to obtain cross-correlated data; and determining, based at least on said cross-correlated data, a type and/or amount of moisture on the exterior surface of the vehicle window.
 3. The method of claim 2, wherein said cross-correlating is performed after said autocorrelating when certain condition(s) is/are met, and wherein said determining based on at least the cross-correlated data determines the type of moisture on the window, where the type is one or more of: light rain, heavy rain, fog, sleet, snow, ice, and wherein the vehicle window is a windshield or a backlite.
 4. The method of claim 1, further comprising sigma-delta modulating the data relating to the two capacitors, thereby providing analog-to-digital conversion and digital data, and wherein said autocorrelating uses the digital data.
 5. The method of claim 1, further comprising checking said autocorrelated data for negative values.
 6. The method of claim 5, wherein, when the autocorrelated data has negative values, indicating that it is not raining and/or parking at least one wiper of the vehicle.
 7. The method of claim 1, further comprising calculating whether a gradient of an autocorrelation curve associated with said autocorrelated data is greater than a predetermined value.
 8. The method of claim 7, wherein when the gradient is not greater than the predetermined value, then indicating that it is not raining and/or parking at least one wiper of the vehicle.
 9. The method of claim 1, further comprising determining whether there is a match or a substantial match between an autocorrelation curve associated with the autocorrelated data and one or more predetermined autocorrelation curve(s) stored in a database.
 10. The method of claim 9, further comprising, when there is a match or substantial match between the autocorrelation curve associated with the autocorrelated data and the predetermined autocorrelation curve, indicating that it is not raining, parking at least one wiper of the vehicle and/or not actuating wipers of the vehicle.
 11. The method of claim 2, wherein said cross-correlating step is only performed when all of the following conditions are met: (a) the autocorrelated data has no negative values; (b) a gradient of an autocorrelation curve associated with said autocorrelated data is greater than a predetermined value; and (c) the shape of the autocorrelation curve associated with the autocorrelated data is different than a predetermined autocorrelation curve associated with normalized non-disturbed autocorrelation data.
 12. The method of claim 2, further comprising determining a symmetry level of a cross-correlation curve associated with the cross-correlated data.
 13. The method of claim 1, wherein a capacitive array includes at least four capacitors, and autocorrelating data relating to each of the four capacitors to obtain autocorrelated data used for determining whether moisture is present.
 14. The method of claim 1, wherein the data relating to the at least two capacitors, analog or digital, is received from circuitry that receives and/or reads capacitance data from the at least two capacitors, and wherein said capacitors are formed based on a fractal pattern.
 15. The method of claim 1, wherein the data relating to the at least two capacitors may be analog or digital and is output from circuitry that: (a) receives and/or reads data and/or signals from the at least two capacitors, and/or (b) includes a capacitor(s) or other circuit element(s) that mimics or substantially mimics one or more of charging, discharging and/or capacitance of the at least two capacitors.
 16. The method of claim 1, wherein each of the capacitors comprises first and second spaced apart electrodes that are substantially coplanar with one another.
 17. The method of claim 1, wherein at least one capacitor electrode of each of the two capacitors is floating so that the capacitors are isolated from ground.
 18. A moisture sensor for sensing the presence of moisture on a vehicle window, the moisture sensor comprising: at least two moisture sensing capacitors which are affected by moisture present on a surface of the vehicle window; means for autocorrelating data relating to at least one of the capacitors to obtain autocorrelated data; and means for determining, based at least on said autocorrelated data, whether moisture is present on the vehicle window.
 19. The moisture sensor of claim 18, further comprising: means for cross-correlating data relating to the two capacitors so as to correlate data from different capacitors to obtain cross-correlated data; and means for determining, based at least on said cross-correlated data, a type and/or amount of moisture on the window.
 20. The moisture sensor of claim 19, further comprising means for determining how fast to operate at least one wiper of the vehicle based on the cross-correlated data.
 21. The moisture sensor of claim 18, wherein the data relating to the at least two capacitors is digital and has been sigma-delta modulated prior to reaching the means for autocorrelating.
 22. The moisture sensor of claim 18, further comprising means for checking said autocorrelated data for negative values, and means for, when the autocorrelated data has negative values, indicating that it is not raining and/or parking wipers of the vehicle.
 23. The moisture sensor of claim 18, further comprising means for calculating whether a gradient of an autocorrelation curve associated with said autocorrelated data is greater than a predetermined value(s).
 24. The moisture sensor of claim 18, wherein each of the capacitors comprises first and second spaced apart electrodes that are substantially coplanar with one another.
 25. The moisture sensor of claim 18, wherein at least one capacitor electrode of each of the two capacitors is floating so that the capacitors are isolated from ground.
 26. A method of sensing the presence of moisture on a vehicle window such as a windshield or backlite, the method comprising: correlating capacitor data to obtain correlated data; determining, based at least on said correlated data, (a) whether moisture is present on an exterior surface of the vehicle window, and/or (b) a type and/or amount of material present on an exterior surface of the vehicle window.
 27. The method of claim 26, wherein said correlating is autocorrelating and/or cross-correlating.
 28. A method of engaging windshield wipers in response to detected rain, the method comprising: receiving data relating to a capacitive array including at least two capacitors supported by a vehicle windshield; mathematically comparing data relating to one or more of the capacitors with data from that same capacitor, and based at least on said comparing determining whether it is raining; mathematically comparing data relating to different capacitors of the array to obtain mathematically compared data from different capacitors, and based at least thereon determining how fast to operate the wipers of the vehicle and/or an amount of rain falling.
 29. A rain sensor comprising: at least two sensing devices that are affected by rain on a surface of a window; circuitry that provides an output related to the sensing devices; and at least one correlating engine that (a) autocorrelates information from said circuitry to determine whether rain is present, and/or (b) cross-correlates information from said circuitry to determine how fast to operate at least one wiper of a vehicle and/or an amount of rain. 